Agricultural and FoodEconomics

Cavicchioli et al. Agricultural and Food Economics (2019) 7:10 https://doi.org/10.1186/s40100-019-0129-5

RESEARCH Open Access

Does the future of a farm depend on its

neighbourhood? Evidence on intra-familysuccession among fruit and vegetable farmsin Italy Daniele Cavicchioli* , Danilo Bertoni, Dario Gianfranco Frisio and Roberto Pretolani

* Correspondence: daniele.cavicchioli@unimi.itDepartment of EnvironmentalScience and Policy, Università degliStudi di Milano, Via Celoria, 2, 20133Milan, Italy

©Lpi

Abstract

The transfer of farm activity over time occurs through different pathways, amongwhich the more frequent is intra-family farm succession. Thus, better information onfarm succession determinants is crucial for understanding farm succession andinforming appropriate sectoral policies. To date, substantial research has focused onthe effect of farm, farmer and potential heir features on farm succession, while therole played by socio-economic conditions around a farm has been relatively lessexamined. Building on previous contributions, the present paper considers farmsuccession as the opposite of labour migration out of the agricultural sector. Thus,the effect of the labour market and surrounding conditions (LMSC) around a farm onits succession probability is explored. The aim of this paper is therefore to explorewhether and to what extent the inclusion of LMSC variables may contribute to abetter understanding of farm succession. Using data from a sample of 266 fruit andvegetable farms (gathered for informative purposes by a producers’ organizationconsortium), empirical evidence that LMSC variables play an important role inexplaining the succession probability in these types of farms is provided. Specifically,the results show that (i) including LMSC variables in a farm succession analysisincreases the explanatory power and robustness of the model estimates; (ii) LMSCvariables have a non-linear effect on succession; and (iii) some explanatory variables(farmer education and farm age, specialization and dimension) are significant acrossvarious specifications, while other variables (farmer age, territorial location anddistance of a farm from its producer organization) change their sign and/orsignificance when LMSC variables are included in the model. As a consequence, ourfindings suggest that LMSC variables should be included in farm succession andlabour market analysis to provide a better estimate of farm succession probability.

Keywords: Farm transfer, Local labour market, Out-farm migration, Occupationalchoice theory

JEL classification codes: J62, J43, Q12, R12

The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 Internationalicense (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,rovided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, andndicate if changes were made.

Cavicchioli et al. Agricultural and Food Economics (2019) 7:10 Page 2 of 17

BackgroundAgriculture in Italy and Europe involves farms that are predominantly family-owned

(Graeub et al. 2016). For this reason, intra-family succession is the preferred mechan-

ism to transfer farm management to future generations (Lobley et al., 2010; Leonard

et al. 2017; Chiswell 2018). Even if evidence of farm performance after intra-family

succession is not univocal (Bertoni et al. 2017; Carillo et al. 2013), generational

turnover and renewal in agriculture is still largely assured by this process. Thus, an

in-depth analysis of the mechanisms and determinants of intra-family succession is

relevant to addressing the multiple challenges that Italian and European agriculture will

face in future decades.

The relevance of this issue is based on at least three important issues that are widely

debated in the literature: (i) the capacity of intra-family farm succession to ensure an

adequate turnover in agriculture and to address the ageing population of farmers, (ii)

the consequences of high/low rates of farm succession, and finally, (iii) the relationship

between farm succession and rural migration.

The first issue pertains to the progressive ageing of the farmer population, particu-

larly in developed countries where there is a shortage of young farmers, or even a farm

succession crisis (Zagata and Sutherland 2015; Burton and Fischer 2015). This problem

is evident in Italy, where according to Eurostat data from 2016, 41% of farms,

representing 27% of the total agricultural area, are managed by farmers aged 65 years

or over (65% of farms and 50% of the agricultural area managed farmers aged 55 years

or over). Whether a succession crisis is taking place in the agricultural sector is

debatable, considering that official statistics do not record information on farm

succession. Based on information from ad hoc surveys, the FARMSTRANSFER project

(Errington 1998; Uchiyama et al. 2008; Lobley et al., 2010; Chiswell and Lobley 2015)

indicates that succession rates are satisfactory. Nevertheless, in particular contexts such

as marginal areas or areas with a high incidence of small farms, more critical situations

may emerge. Similarly, Zagata and Sutherland (2015) examined young farmer shortages

in Europe using Eurostat data.

Another topic of debate in terms of succession in agriculture pertains to the

potential consequences of the lack of successors in agriculture and for ageing

farmers. Opinions differ on this point. Some believe that low succession rates

should not necessarily be considered a negative phenomenon, as in more product-

ive areas, farms without successors can be absorbed by farms that are maintained,

favouring economies of scale (Chiswell and Lobley 2015; Glauben et al. 2006).

Other authors argue that, especially in marginal areas, the lack of succession can

translate into abandonment of territory and loss of farm-specific knowledge and

human capital accumulated and transmitted by previous generations, with a risk of

environmental and territorial degradation (MacDonald et al. 2000; Corsi 2009;

Raggi et al. 2013; Demartini et al. 2015).

However, several authors agree that young farmers are more inclined towards entre-

preneurship (Vesala and Vesala 2010; McDonald et al. 2014; Stenholm and Hytti 2014),

farm business diversification (McElwee and Bosworth 2010; Grubbström et al. 2014;

Suess-Reyes and Fuetsch 2016; Ohe 2018) and adoption of eco-sustainable farming

practices (Van Passel et al. 2007; Bertoni et al. 2011; Paracchini et al. 2015; Hamilton

et al. 2015; Suess-Reyes and Fuetsch 2016).

Cavicchioli et al. Agricultural and Food Economics (2019) 7:10 Page 3 of 17

Finally, the issue of farm succession clearly intersects with the wider abandonment of

rural areas by younger people. Although the two phenomena do not completely

overlap, they are connected, and farm succession trajectories can be considered a part

of this larger phenomenon, especially in areas characterized by high agricultural

employment (Bertoni and Cavicchioli 2016b). Several studies analysed the motivations

behind the intentions of potential heirs to abandon agricultural activity and/or rural

areas (Morais et al. 2017, 2018; Bednaříková et al. 2016; Chen et al. 2014; Bjarnason

and Thorlindsson 2006). In particular, this trend seems to be strengthened among

young people with a higher level of education (Bednaríková et al., 2016) and, other

things being equal, women (Leibert 2016; Johansson 2016).

For the above-mentioned reasons, understanding the mechanisms and determinants

of farm succession is relevant for assuring continuity in agricultural activities. A large

part of the literature on farm succession is devoted to analysing the process of farm

succession using both qualitative and quantitative approaches (Bertoni and Cavicchioli

2016b). Quantitative papers on farm succession rely on estimating the timing of succes-

sion, the effect of succession on farm assets/performance and mainly the probability

and determinants of farm succession. Specifically, this last type of literature focuses on

isolating the factors affecting the probability of farm transfer. Such analyses exploit

limited dependent variable regression (probit or logit) using the event of farm succes-

sion as a dependent variable (1, succession takes place; 0, succession does not take

place). This type of event may be observed directly (following a sample or a population

of farms over a long time span, Stiglbauer and Weiss 2000) or may be inferred by

asking a farmer his/her expectations about farm transfer (Glauben et al., 2004). In the

former case, the information is more reliable, while data based on farmer statements

may be inconsistent with respect to the actual event of succession (Väre et al. 2010).

Most of the analyses have been carried out at the farm level, while few have focused on

the probability that each potential heir in a family farm will take over the farm

(Simeone 2006; Mann 2007; Aldanondo Ochoa et al. 2007).

The main part of farm succession literature focuses on the effect of farm, farmer and

potential heirs features on the probability that succession takes place (Stiglbauer and

Weiss 2000; Kimhi and Nachlieli 2001; Simeone 2006; Glauben et al. 2009; Cavicchioli

et al. 2015), paying less attention to the effect exerted by socio-economic conditions

around the farms examined, with some notable exceptions (Glauben et al., 2004,

Aldanondo Ochoa et al. 2007; Corsi 2009; Kerbler 2008). To account more explicitly

for the effect of the economic environment around the farm, some recent contributions

(Bertoni and Cavicchioli 2016a; Cavicchioli et al. 2018) have treated the phenomenon

of farm succession as the opposite choice with respect to searching for alternative em-

ployment outside of the farm sector. This decision has been previously modelled within

the occupational choice theory (OCT—Todaro 1969; Harris and Todaro 1970;

Mundlak, 1978; Barkley 1990 and Larson and Mundlak 1997). According to this theory,

the probability of searching and finding a job in another sector is (linearly) fostered by

the income gap between sectors: the larger the spread between the sectors (e.g., agricul-

ture and non-agricultural activities) is, the higher the incentive for an individual to mi-

grate out of the sector of origin. OCT also states that the income gap between sectors

is a necessary but not sufficient condition for labour mobility, as other variables may

increase or decrease the transaction costs for finding employment outside of the

Cavicchioli et al. Agricultural and Food Economics (2019) 7:10 Page 4 of 17

original sector. Such variables are population density, employment rate, and the relative

dimensions (in labour size) of the economic sectors where the migration takes place.

Considering, for example, labour migration from agricultural to non-agricultural sec-

tors, such a process, according to OCT, is favoured in areas with higher population

densities that offer more occasions to find a job. Similarly, the higher the employment

rate in the local labour market, the higher the probability that agricultural workers will

find jobs in non-agricultural sectors. Finally, the smaller the relative labour size of the

agricultural sector (intended as the number of agricultural workers with respect to

non-agricultural workers), the higher the employment opportunities will be in

non-agricultural sectors for labourers coming from farming activities.

A recent analysis of the effect of agricultural payments on off-farm labour migration

was based on OCT (Olper et al. 2014), with results consistent with theoretical expecta-

tions. Treating farm succession as an alternative to off-farm migration allows a bridge

between a traditional farm succession analysis and OCT. Because farm transfer represents

the opposite choice with respect to finding a job outside of the agricultural sector, the

expected effect of local labour market variables (income gap, population density, employ-

ment and relative labour size of an agricultural sector) on farm transfer should be the

opposite of those predicted by OCT. Thus, if OCT predicts a positive effect of income

gap, population density, employment rate and relative labour size of the farm sector on

agricultural labour migration, then increasing values of these variables are expected to

discourage farm succession. The use of the variables suggested by OCT in farm succes-

sion analyses has yielded some unexpected results; according to OCT, local labour market

variables should exert a linear effect on labour migration, but the effect of these variables

in farm succession analyses were non-linear (Bertoni and Cavicchioli 2016a; Cavicchioli

et al. 2018). The authors explained such unexpected outcomes as a combination of

pro-succession and anti-succession effects for increasing values of local labour market

variables.

The aim of this paper is therefore to better understand whether and to what extent

the inclusion of LMSC variables may contribute to a better understanding of farm

succession. This paper is based on a traditional farm succession analysis, as it uses farm

and farmer features to explain farm transfer; furthermore, in continuity with recent

studies (Bertoni and Cavicchioli 2016a; Cavicchioli et al. 2018), it contributes to the lit-

erature by accounting explicitly for the interaction between farm and farmer character-

istics with labour market and surrounding conditions (LMSC) on farm succession

predictions. By including the latter group of variables, intra-family farm succession was

modelled as an alternative to searching for employment in non-agricultural sectors.

With respect to previous studies that used OCT in farm succession analyses, in this

study, the empirical exercise was carried out on a larger sample of fruit and vegetable

farms. An additional contribution of this study, in comparison to similar previous

analysis, is to assess whether and to what extent the inclusion of LMSC variables

improves the prediction of farm succession in terms of estimation accuracy.

This analysis explores the following factors: (i) whether LMSC variables affect

farm succession, (ii) whether their effect is linear or non-linear, and (iii) whether

and to what extent the inclusion of such variables improves the prediction of farm

succession in comparison to a traditional farm succession analysis, based mainly on

farm and farmer features.

Cavicchioli et al. Agricultural and Food Economics (2019) 7:10 Page 5 of 17

The remainder of the paper is organized as follows: the “Data and methodology” sec-

tion shows the sample data and methodology used to carry out the empirical exercise,

presenting the different models (with and without LMSC variables) and explaining the

post-estimation tests for model comparison. The “Results and discussion” section pre-

sents and discusses the results of the different models used to predict farm succession,

in addition with post-estimation comparisons. The “Concluding remarks” section out-

lines the main conclusions.

Data and methodologyData and variables

As mentioned in the introduction, detecting farm succession is difficult, considering

the lack of official data on this process. Thus, the present analysis focused on a group

of farms for which such information was available.

The empirical exercise to test the above-mentioned hypotheses was carried out

using data from a 2010 survey of fruit and vegetable farms belonging to a consor-

tium of producers’ organizations (POs). It is worth noting that such a survey was

originally designed by the PO consortium for informative purposes and not for

research aims. Therefore, the survey was intended to provide a detailed picture of

farms belonging to the consortium, with detailed questions on crop mix and

production. Researchers have been involved only in designing the part of the

questionnaire devoted to human capital and farm succession perspectives. The pilot

questionnaire was pre-tested and validated at the beginning of 2010 on a limited

number of farms representing the main typologies of the entire population. The

validated questionnaire was administered to all farms over 2010. Both the pilot and

final questionnaires were administered face-to-face by technicians of the consor-

tium to farm holders. The information on farm succession perspectives allowed us

to infer farm succession probabilities. Unfortunately, as the questionnaire was

mainly designed for informative purposes for the PO consortium, some explanatory

variables, usually employed in farm succession analysis literature, were not

collected. Thus, these missing variables have been replaced by other available prox-

ies in the subsequent analysis.

Furthermore, the database from which data have been drawn covered all the farms

that were members of the PO consortium (603 farms); as farm-level data on farm

membership in producers organizations (and their consortia) are not publicly available,

it is difficult to establish to what extent the collected data may be representative of all

fruit and vegetable farms that are members of POs in Italy. For the same reason, the

extendibility of the results is not straightforward.

From all the surveyed members of the PO consortium (603), a sample of 266 farms

with farm-holders older than 45 years old with at least one child aged 15 years or older

was selected. Such criteria were adopted to ensure that farms that have considered

and/or are planning on farm succession were selected. Thus, we excluded farms with-

out potential successors or siblings that were too young on the family farm.

The event of family business transfer in the sample was treated as a dichotomous

variable, inferred from farm-holder expectation on whether farm succession would take

place in the future. It is worth remembering that such expectations may not result in

Cavicchioli et al. Agricultural and Food Economics (2019) 7:10 Page 6 of 17

actual behaviour (Väre et al. 2010). As the aim of the analysis was to explore the im-

provement in farm succession estimations brought by labour market and surrounding

conditions (LMSC) variables, in comparison to farm and farmer features, both the

former and the latter have been selected as explanatory variables. The description of

the variables is included in Table 1 in addition to the justification for their inclusion in

the analysis, which was based on previous literature.

The explanatory variables in Table 1 are classified as belonging to the farm, farmer or

LMSC variables. Farm and farmer variables have been selected as a compromise be-

tween evidence from previous literature on farm succession (listed in the last column

of Table 1) and data availability. As the survey from which the dataset was developed

was not designed for research purposes, it was necessary to select some proxy variables

that were as similar as possible to those suggested by the literature. In particular,

among farm-level information, available data on farm size, both in economic (Turn-

over_250, expressed as a dummy variable) and in structural terms (Workdays), have

been selected. Furthermore, two dummy variables (RPFV farm and Fruit farm) were

selected to capture the heterogeneity in farm production within the sample.

Labour market and surrounding conditions (LMSC) variables (income gap, popula-

tion density, employment rate and share of agricultural labour) were calculated at the

local labour system level. According to the Italian Institute for Statistics (ISTAT), a

local labour system is an area (groups of municipalities) with homogeneous features in

terms of supply and demand of labour (for further information, see www.istat.it/en/

archive/142790). Therefore, LMSC variables have been calculated and associated with

the local labour system of each farm in the sample. Notably, in Table 1, variables not

suggested by occupational choice theory, such as Distance (the distance of the farm

from the headquarter of the producer organization) and various territorial locations of

the farm (Hills, Mountain and regional dummies), are classified as LMSC variables.

The inclusion of such variables is suggested by the literature and is necessary to capture

site-specific characteristics of the areas surrounding a farm. Even if the present contri-

bution takes advantage of the theoretical insights from occupational choice theory,

there is not yet a unifying theoretical framework suggesting which variables should be

included or excluded in the empirical exercise to test farm succession determinants.

Thus, among the variables available in the dataset, we selected those that were relevant

in previous literature under different specifications. In so doing, it was possible to test

the three research questions mentioned above and to isolate farm succession determi-

nants with effects that are more stable across various specifications.

The sample contains 266 farms of which 113 with farmers that declared succession

and 153 with farmers that did not expect an intra-family succession of the farm busi-

ness. Table 2 reports descriptive statistics for each variable of the sample and is split

into farms with and without (expected) succession.

Methodology

As described in the background section, the main contribution and emphasis of the

present paper is to explore whether and to what extent labour market variables and

surrounding conditions (LMSC) around a farm affect succession probability and im-

prove its estimation. These research questions informed the empirical strategy that was

Table

1Definition

ofvariables

used

intheanalysis

Variablecatego

ryVariable

Definition

Unitof

measure

Previous

stud

iesusingsimilarvariables

(effect

onsuccession

)1

Dep

.variable

Succession

Farm

erthinks

that

thene

xtge

neratio

nwill

take

over

thefarm

1=yes;0=no

Farm

erFarm

erage

Age

ofthefarm

-holde

rAge

Corsi2009

(BS);G

laub

enet

al.,2004

(BS);A

ldanon

doOchoa

etal.2007(US);Kim

hiand

Nachlieli2001

(BS);Stig

lbauer

andWeiss

2000

(BS)

Farm

erde

gree

Farm

erhasade

gree

1=yes;0=no

Simeo

ne2006

(+);Corsi2009

(−);Mishraet

al.2010(−)Berton

iand

Cavicchioli2016a

(−)

Farm

erchildren

Num

berof

farm

ers’children>15

yearsoldin

thefarm

Num

berof

children

Aldanon

doOchoa

etal.2007(−);Cavicchioliet

al.2015(−);Mann2007

(+)

Farm

Turnover_250

Thefarm

annu

alturnover

isover

250,000EU

R1=turnover

>250,000

EUR;0=othe

rwise

Corsi2009

(+);MishraandEl-Osta2008

(+);Aldanon

doOchoa

etal.2007(+);Kerbler

2008

(+)

Farm

duratio

nYearssincethefarm

began

Years

Berton

iand

Cavicchioli2016a(+)

RPFV

farm

Farm

type

:ready

prep

ared

fresh

vege

tables

(RPFV)

1=yes;0=no

Kimhi

andNachlieli2001

(−);Berton

iand

Cavicchioli2016a(+)

Fruitfarm

Farm

type

:fruitprod

uctio

n1=yes;0=no

Emplwork

Shareof

employed

workeddays

ontotalann

ual

workeddays

inthefarm

%Kerbler2008

(−)

Workdays

Theannu

alworkeddays

inthefarm

both

byho

lder

family

andem

ployees

Days

Aldanon

doOchoa

etal.2007(+);Glaub

enet

al.,2004

(+);Kimhi

andNachlieli

2001

(−)

Labo

urmarket

andsurrou

nding

cond

ition

s(LMSC

)

Distance

Distancefro

mthehe

adqu

artersof

theprod

ucer

organizatio

nKilometres

Aldanon

doOchoa

etal.2007(−)

Popd

ens

Popu

latio

nde

nsity

atthelocallabou

rsystem

slevel

Inhabitantspe

rsquare

kilometre

Alasiaet

al.2009(+);Olper

etal.2014(−);Berton

iand

Cavicchioli2016a(US)

Empl

Employmen

trate

atthelocallabou

rsystem

slevel

%Corsi2009

(−);Barkley1990

(+);Alasiaet

al.,2009

(−);Olper

etal.2014(+);Cavicchioli

etal.2018(BS)

Agrshare

Shareof

agriculturalemploymen

ton

total

employmen

tat

thelocallabou

rsystem

slevel

%Barkley1990

(−);Larson

andMun

dlak

1997

(−);Corsi2009

(+);Olper

etal.2014(+)

Incgap

Incomegapbe

tweenno

n-agriculturalsectors

andagriculturalsectorin

each

province

(NUTS

3)Thou

sand

sEU

R(GVA

/worker)

Barkley1990

(−);Larson

andMun

dlak

1997

(−);Olper

etal.2014(−);Berton

iand

Cavicchioli2016a(BS)

Cavicchioli et al. Agricultural and Food Economics (2019) 7:10 Page 7 of 17

Table

1Definition

ofvariables

used

intheanalysis(Con

tinued)

Variablecatego

ryVariable

Definition

Unitof

measure

Previous

stud

iesusingsimilarvariables

(effect

onsuccession

)1

Hills

Farm

islocatedin

thehills

1=yes;0=no

Corsi2009

Mou

ntain

Farm

islocatedon

themou

ntains

1=yes;0=no

Glaub

enet

al.,2004

(−)

Region

aldu

mmies

Farm

locatio

nin

aNUTS

2region

(omitted

:Lom

bardy)

1=yes;0=no

1(+)alin

earpo

sitiv

eeffect,(−)alin

earne

gativ

eeffect,(BS)abe

ll-shap

edno

n-lin

eareffect,(US)

au-shap

edno

n-lin

eareffect

Cavicchioli et al. Agricultural and Food Economics (2019) 7:10 Page 8 of 17

Table 2 Descriptive statistics of the sample

Variable Total farms in the sample(n = 266)

Farms with succession(n = 113)

Farms without succession(n = 153)

Mean Std. dev. Mean Std. dev. Mean Std. dev.

Succession 0.42 0.50

Farmer age 56.04 9.56 55.76 10.63 56.29 8.68

Farmer degree 0.05 0.21 0.04 0.19 0.05 0.22

Farmer children 2.55 1.56 2.93 1.76 2.30 1.34

Turnover_250 0.30 0.46 0.17 0.38 0.40 0.49

Farm duration 32.85 22.71 35.68 23.39 29.68 21.24

RPFV farm 0.23 0.42 0.38 0.49 0.12 0.33

Fruit farm 0.38 0.49 0.22 0.42 0.52 0.50

Emplwork 25.73 32.93 33.71 34.14 20.00 31.04

Workdays 966.5 1206.1 1332.4 1407.6 696.3 965.6

Distance 55.29 138.31 66.61 149.29 49.43 132.98

Popdens 298.2 429.8 365.4 403.9 242.4 420.2

Empl 47.64 4.09 46.89 5.00 48.07 3.28

Agrshare 5.85 3.29 5.53 2.92 6.04 3.53

Incgap 29.68 9.27 27.22 8.99 31.70 9.15

Hills 0.04 0.21 0.09 0.29 0.01 0.11

Mountain 0.38 0.49 0.24 0.43 0.50 0.50

Regional dummies

Campania 0.13 0.33 0.22 0.42 0.07 0.25

Piemonte 0.06 0.24 0.03 0.16 0.09 0.29

Veneto 0.03 0.17 0.05 0.23 0.01 0.11

Emilia-Romagna 0.03 0.18 0.04 0.21 0.03 0.16

Cavicchioli et al. Agricultural and Food Economics (2019) 7:10 Page 9 of 17

based on a limited dependent variable (probit) regression. This type of regression

analysis yields estimated coefficients (odds ratios) that measure the effect of each

explanatory variable on the succession probability. Therefore, three different specifica-

tions have been estimated:

� First specification (models 1, 2 and 3), including only farm and farm variables

� Second specification (model 4), including farm and farm variables and LMSC

variables in linear form, as suggested by occupational choice theory

� Third specification (models 5, 6 and 7), including farm and farm variables and

LMSC variables in non-linear form

The six models estimated in the first and third specifications differ for the introduc-

tion of squared terms in some continuous variables to test their possible non-linear

effects. Overall, seven models have been estimated. For brevity purposes, the results of

only three models (models 3, 4 and 7) are presented, one for each specification, in

addition to post-estimation statistics. For the first and third specifications, the models

that perform better in terms of prediction accuracy (pseudo R-square and share of

correctly classified) were selected. The comparison of all seven models, in addition to

their post-estimation statistics, is available as Additional file 1.

Cavicchioli et al. Agricultural and Food Economics (2019) 7:10 Page 10 of 17

In the first specification (models 1–3), we tested the effects of farm and farmer

characteristics and of farm location in the hills or in the mountains (also defined

as altimetry) and regional dummies. The effect of altimetry dummies was

measured compared to the excluded altimetric band (plane). For instance, a

positive and significant coefficient of “Hill” indicates a higher succession probabil-

ity for farms located in hilly areas with respect to those in plain areas. The same

reasoning applies to regional dummies, whose effect was compared to the

excluded region (Lombardy).

As in the literature, some continuous variables (such as farmer age) are found to

exert a non-linear effect on succession, which was tested both in models 1–3 and in

models 4–6 for some continuous variables. In the second specification (model 4),

LMSC variables—Popdens, Empl, Agrshare and Incgap—are entered in linear form, as

suggested by occupational choice theory. In models 5–7, the same variables are tested

in polynomial (non-linear) form. This process is justified by previous findings (Bertoni

and Cavicchioli 2016a, 2016b; Cavicchioli et al. 2018).

To compare different models, some statistical criteria that are widely used in the

context of model selection are considered. Specifically, the pseudo R-square (see

McFadden (1973), which is based on the likelihood ratio test, the scoring function (see

Akaike 1974 and Schwarz 1978) and finally some classification results from the

confusion matrix are used (Provost et al. 1998).

The pseudo R-square used in this analysis is McFadden’s test. This test is based on a

comparison between the log-likelihoods of two specifications: the full-model with

predictors and the intercept model. Therefore, McFadden’s R-squared measure can be

defined as follows:

R2McFadden ¼ 1−

ln L̂ Mfull−modelð Þln L̂ Mintercept

� � ð1Þ

where L̂ denotes the maximized estimated likelihood value, while Mfull −model and

Mintercept denote the full-model predictors and the model with only an intercept,

respectively. Thus, the ratio of the log-likelihoods suggests the level of improve-

ment over the intercept model offered by the full model. Moreover, a small ratio

of the likelihoods indicates that the full model provides a better fit than the inter-

cept model, implying that McFadden’s measure is higher for the model with the

greater estimate of likelihood function.

The Akaike information criterion (AIC) and the Bayesian information criterion (BIC)

represent alternative model selection tools. They are defined as follows:

AIC ¼ −2 ln L̂� �þ 2k ð2Þ

BIC ¼ −2 ln L̂� �þ 2 ln Nkð Þ ð3Þ

where lnðL̂Þ denotes the maximized estimated log-likelihood value related to the full-

model predictors, and k and N are the total number of estimated parameters and obser-

vations, respectively. Both the AIC and the BIC indicators represent badness of fit

measures; therefore, the optimal model is selected based on the minimum AIC and/or

BIC. It is important to highlight that the BIC shows a larger penalty term to address

the issue of the overfitting model.

Cavicchioli et al. Agricultural and Food Economics (2019) 7:10 Page 11 of 17

Finally, the confusion matrix consists of a predictive classification table, with the

representation shown in Table 3:

Given the context of our analysis, we define an “event” as r farm succession occurring

or not occurring. If farm succession takes place, then the event is equal to 1. If farm

succession does not take place, then the event is equal to 0. Therefore, the entries of

the matrix, A, B, C and D have different meanings. Specifically, A is the number of cor-

rect predictions that farm succession takes place, B is the number of incorrect predic-

tions that farm succession takes place, C is the number of incorrect predictions that

farm succession does not take place, and D is the number of correct predictions that

farm succession does not take place.

Thus, the representation of the confusion matrix allows us to compute, for each

estimated model, three types of conditional probabilities:

1. The sensitivity, which is defined as the A/(A + C) proportion of events

2. The specificity, which is the (D/(B + D)) proportion of non-events

3. The accuracy (also known as the share of correctly classified), which is the ((A +D)/ N)

proportion of events correctly predicted by the model

Given the context of our analysis, the above-mentioned conditional probabilities

(sensitivity, specificity and accuracy) in addition to the pseudo R-square and the

information criteria (AIC and BIC) provide an intuitive measure of comparison

among the models.

Results and discussionThe main results of the model estimates of farm succession are presented in Table 4,

based on the estimation strategy presented in the “Methodology” section. Table 4

reports only three of the seven models estimated, which are presented in

Additional file 1: Annex 1, as a robustness check. Parameter estimates in bold denote

those variables with a stable effect, in terms of sign and significance, across the models.

There are some variables that had an effect on intra-family farm succession robust

across various specifications in terms of sign and significance. The graduation of

farm-holders exerts a negative effect on the probability of farm succession, in contrast to

the results of Simeone (2006), who examined a sample of 100 farmers in central Italy but

in line with some studies that used a similar proxy, considering parents with at least a

high school diploma (Corsi 2009; Mishra et al. 2010). Different effects of the same variable

may be explained by the specificity of the contexts examined. More generally, a possible

explanation of the present results relies on a higher probability that a graduate farmer

pushes his/her children to graduate, and graduation, especially in non-agricultural

subjects, would increase their propensity to abandon agricultural activity.

The probability of farm succession is higher for older farms (coefficient positive and

linear, consistent with Bertoni and Cavicchioli 2016a). Specialization in fruit or

ready-prepared fresh vegetables results in the probability of having successors negative

or positive, respectively. The coefficient of the share of hired work is negative and

consistent with Kerbler (2008), who tested the same variable on a sample of 789

Slovenian farmers. Additionally, farm size had a stable effect on succession, even if

divergent in financial and structural terms. Succession probability is lower in farms

Table 3 Theoretical confusion matrix comparing the number of events observed and predicted byan estimated model

Predicted/observed farm succession events Observed farm succession events

Farm successiontakes place

Farm succession doesnot take place

Total observed farmsuccession events

Predicted farmsuccession events

Farm successiontakes place

A B A + B

Farm succession doesnot take place

C D C + D

Total predicted farmsuccession events

A + C B + D N

Table 4 Results of probit regression

Farm and farmer variables; no labourmarket and surrounding condition(LMSC) variables (model 3)

Farm and farmervariables; LMSC variablesin linear form (model 4)

Farm and farmer variables;LMSC variables in non-linear form (model 7)

Farmer age 0.0121 0.0118 − 0.1481*

Farmer age_sq – – 0.0013*

Farmer degree − 0.9126*** − 0.9698*** − 0.8957***

Farmer children 0.0372 0.092 0.1379**

Turnover_250 − 0.3030* − 0.3309* − 0.3785**

Farm duration 0.0116*** 0.0108*** 0.01***

RPFV farm 0.8196** 0.4348 0.8985***

Fruit farm − 1.3773*** − 1.2117** − 1.1348**

Emplwork − 0.0108*** − 0.0113** − 0.0104*

Workdays 0.0007** 0.0006** 0.0007**

Workdays_sq 0.0000** 0.0000* 0.0000**

Distance 0.0080** −0.0004 − 0.0008***

Distance_sq 0.0000** – –

Popdens – 0.0000 − 0.0036***

Popdens_sq – – 0.0000***

Empl – 0.0164 2.7195***

Empl_sq – – − 0.025***

Agrshare – − 11.2887*** − 4.8662

Incgap – 0.1382*** − 1.3349***

Incgap_sq – 0.0335***

Hills 0.7892 1.9286*** 2.3695***

Mountain 1.4278*** − 1.1795 − 10.8799***

Campania 0.6311* 1.0965 7.0349***

Piemonte −1.291*** − 2.2433*** − 7.041***

Veneto 0.1133 2.6116*** 1.6324**

Emilia-Romagna − 0.0911 0.5548 − 0.9747

Constant − 1.8808*** − 5.0828 − 57.2248**

N obs 259 259 259

Pseudo R2 23% 26% 30%

*, ** and ***, denote significance at 10%, 5% and 1%, respectivelyThe table reports three of seven different model specifications. The complete version with all the models is presented inAdditional file 1: Annex 1The parameter estimates whose sign and significance are stable across different specifications are presented in italics(those not presented in Table 3 are available as supplemental material)

Cavicchioli et al. Agricultural and Food Economics (2019) 7:10 Page 12 of 17

Cavicchioli et al. Agricultural and Food Economics (2019) 7:10 Page 13 of 17

with turnover beyond 250,000 euros, which is not consistent with previous findings

(Mishra and El-Osta 2008; Aldanondo Ochoa et al. 2007). On the other hand, a stable

positive effect of the structural dimensions of a farm, approximated by the number of

workdays, is observable.

As occupational choice theory suggests a linear effect of LMSC variables on

labour migration (and then, in the opposite manner, on succession), LMSC

variables have been entered in linear terms (model 4), finding a positive linear role

of income gap (between agricultural and non-agricultural sectors), negative linear

role of share of agricultural employment and non-significant effect of employment

rate and population density. According to the second research question (and in

line with Bertoni and Cavicchioli 2016a and Cavicchioli et al. 2018), the non-linear

effects of such variables have been explored. Thus, three (income gap, employment

rate and population density) of the four LMSC variables were significant both in

linear and in squared terms, while the share of agricultural employment was not

significant (neither linear nor squared). In line with similar previous findings

(Bertoni and Cavicchioli 2016a; Cavicchioli et al. 2018), the explanation of such re-

sults relies on a combination of pro-succession and anti-succession effects due to

the specific features of such farms. The pro-succession effects of LMSC variables

rely on increased market opportunities for fruit and vegetable farms nearer to

densely populated and wealthier areas (having higher employment rates and larger

income gaps compared to agricultural areas). Another possible explanation of

pro-succession effects relies on better services for the family farm that would be

encouraged to continue its activity. The anti-succession effects exerted by LMSC

variables are those predicted by the occupational choice theory: if increasing values

of Popdens, Empl and Incgap favor out-farm migration, then for the same reason,

they will discourage farm succession. Interestingly, the introduction of LMSC vari-

ables restored the significance of some farm and farmer variables: farmer age

(non-linear u shaped, even if significant at 10%), number of children in the family

farm (positive, in line with Mann 2007 but in contrast to Aldanondo Ochoa et al.

2007 and Cavicchioli et al. 2015), distance of the farm from the producers

organization where the product is delivered (negative, in line with Aldanondo

Ochoa et al. 2007) and farm location in hilly areas (positive, in line with Corsi

2009, in contrast to Glauben et al. 2004). The same effect occurred with the re-

gional dummy Campania, whose magnitude and significance increase consistently.

Furthermore, farm location in mountainous areas changed sign (from positive to

negative, increasing its magnitude) after the inclusion of contextual variables. The

results presented point to a non-linear effect of LMSC variables on farm succes-

sion, as in the linear specification (model 4) where only two of the four LMSC var-

iables were significant.

To compare the different models presented and to explore the contribution of the

LMSC variables in improving farm succession predictions, some post-estimation

statistics for model comparisons have been carried out. The statistics are presented in

Table 5 for models 3, 4 and 7, while the same statistics for all the estimated models are

available in Additional file 1: Annex 2. An explanation description of the statistics

presented in Table 5 is included in the last part of the “Methodology” section.

The model evaluation and comparison criteria provide three main results.

Table 5 Post-estimation statistics for model comparison

Farm and farmer variables; no labourmarket and surrounding conditions(LMSC) variables (model 3)

Farm and farmervariables; LMSC variablesin linear form (model 4)

Farm and farmer variables;LMSC variables innon-linear form (model 7)

Pseudo R2 23% 26% 30%

Sensitivity 65% 62% 65%

Specificity 82% 83% 87%

Correctly classified 75% 74% 77%

AIC 312 306 295

BIC 379 384 380

Post-estimation statistics for models 1, 2 5 and 6 are available in Additional file 1: Annex 2

Cavicchioli et al. Agricultural and Food Economics (2019) 7:10 Page 14 of 17

First, for each of the McFadden’s R-squared measures reported in Table 5 and

Additional file 1: Annex 2, there is substantial agreement that model 7 fits the outcome

data better than the other models.

Second, the AIC and BIC are minimized in models 7 and 1, respectively. This

outcome is not surprising because BIC penalizes model complexity more heavily, which

is consistent with the larger number of estimated parameters involved in model 7

(having LMSC variables in level and squared) than in model 1.

Third, the results from the confusion matrix allow us to compare the sensitivity,

specificity and accuracy for each model at a 0.5 cut-off value. These three indicators

have the advantage of assessing the performance of each model in terms of actual and

predicted classifications without being affected by the proportion of farms with succes-

sion. Table 5 shows that despite models 1 and 7 providing the same percentage of

sensitivity (65%), the inclusion of the LMSC variables improves the specificity and the

accuracy of the outcome variable.

Overall, for this analysis, model 7 offers the best fit among the set of candidate models,

and therefore, the inclusion of LMSC variables offers better farm succession predictions.

Concluding remarksThe aim of this paper was to explore whether and to what extent the inclusion of

labour market and surrounding conditions (LMSC) variables may improve a farm

succession analysis. The reason for including such a category of determinants relies on

the already identified intuition that family business transfer may be viewed as an

alternative decision of potential heir(s) compared to finding employment outside of the

agricultural sector.

According to OCT, the latter decision is affected by LMSC variables. The

above-mentioned research question has been addressed empirically by estimating

various models that predict farm succession probability. One model was estimated that

included only farm and farmer features (and without LMSC variables), while in the

second and third models, LMSC variables were included in linear and non-linear form,

respectively. In the second model (linear), only two LMSC variables were significant,

while in the third model (non-linear), three of the LMSC variables were significant,

suggesting that the relationship between farm succession and LMSC variables may be

better modelled in a non-linear fashion rather than as a linear relationship. From the

model comparison, a block of variables emerged (Farmer children, Farm duration, Fruit

farm, Emplwork and Workdays) whose effect was robust across specification, while the

Cavicchioli et al. Agricultural and Food Economics (2019) 7:10 Page 15 of 17

introduction of LMSC variables restored the significant effect of some other variables

(Hills) and changed the sign of some others (Mountain). To investigate and compare

the accuracy of different model specifications (excluding and including LMSC

variables), some post-estimation and model fitting tests were performed. The model in-

cluding LMSC variables in a non-linear form emerged as a better fit in terms of the actual

event of farm succession in terms of pseudo R-squared, specificity, share of correct predic-

tions and one of two information criteria (AIC). For this reason, the present empirical

findings suggest that farm succession analyses are improved by including LMSC variables.

As stated in the data description, given the specific composition of the sample (fruit

and vegetable farms belonging to a PO consortium) and the lack of data on PO affilia-

tions in the underlying population of these farms, both sample representativeness and

result extendibility are not straightforward. Thus, the present analysis should be

repeated on different types of farms and in different areas to obtain a better picture of

the effect of LMSC variables on farm succession. However, the contribution of the

present findings goes beyond the underlying population of our sample (farms members

of POs or fruit and vegetable farms) as it may provide various implications for farm

succession analyses and potentially for farm labour migration studies. First, contextual

variables (LMSC variables) play a role when analyzing the probability of intra-family farm

succession, improving model fit and predictions; second, in specific cases, such as those

examined, the effect of LMSC variables on intra-family transfer is non-linear, unlike those

expected by occupational choice theory; third, the inclusion of contextual variables may

change the significance and/or sign of some farm and farmer variables on succession

probability. These implications suggest considering intra-family farm transfer as part of a

wider problem of labour allocation choice in the agricultural sector that cannot be

examined in isolation with respect to labour, population and economic patterns of other

sectors. Furthermore, potential non-linear effects of labour market and contextual vari-

ables should be explored and taken into account not only in farm succession analysis but

also in empirical estimations based on occupational choice theory, for instance, in the

assessment of the labour effect of agricultural policy. Finally, the present study indirectly

points to the lack of a clear and unifying theoretical framework underpinning the empir-

ical analysis of family farm features affecting succession (child, farmer and farm features).

Such an in-farm theoretical framework of succession should be clearly defined to match

the out-farm migration framework suggested by occupational choice theory.

Additional file

Additional file 1: Annex 1 -Robustness Check across different specifications. Annex 2 - Post-estimation statisticsfor comparison of the 7 estimated models. (DOCX 23 kb)

AcknowledgementsThe present work would not have been possible without data gathered during the research project “AOP UNOLOMBARDIA: ilprimario avanzato—Progetto per lo sviluppo di una struttura a rete cheassista la ‘coopetizione’ tra le filiere ortofrutticoleaderenti ad AOPUNOLOMBARDIA” undertaken by the Rural Development Program of the Lombardy Region 2007–2013.

FundingNot applicable

Availability of data and materialsThe datasets generated during and/or analysed during the current study are not publicly available due to aconfidentiality agreement with subjects involved in the survey.

Cavicchioli et al. Agricultural and Food Economics (2019) 7:10 Page 16 of 17

Authors’ contributionsThe research design was conceived by DC and DB. Data gathering has been carried out by DGF and RP. The“Background” section has been written by DC and DB, the “Data and variables” section by DB, the “Methodology”section by DC, the “Results and discussion” section by DC and the “Concluding remarks” by DC, DB, DGF and RP. Allthe authors read and approved the final manuscript.

Competing interestsThe authors declare that they have no competing interests.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Received: 30 July 2018 Accepted: 7 May 2019

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